mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
234 lines
8.8 KiB
Python
234 lines
8.8 KiB
Python
import os
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from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler, LlamaForCausalLM
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import torch
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from torch.optim import AdamW
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from argparse import ArgumentParser
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from read import read_config
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from accelerate import Accelerator
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from accelerate.utils import DummyScheduler, DummyOptim, set_seed
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from peft import get_peft_model, LoraConfig, TaskType
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from data import load_data
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from torchmetrics import MeanMetric
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from tqdm import tqdm
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import wandb
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torch.backends.cuda.matmul.allow_tf32 = True
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def format_metrics(metrics, split, prefix=""):
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log = f"[{split}]" + prefix
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log += " ".join([f"{key}: {value:.4f}" for key, value in metrics.items()])
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return log
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def evaluate(model, val_dataloader):
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model.eval()
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val_loss = MeanMetric(nan_strategy="error").to(model.device)
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with torch.no_grad():
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for batch in tqdm(val_dataloader):
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loss = model(**batch).loss
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loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
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val_loss.update(loss_values["loss"])
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return val_loss
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def train(accelerator, config):
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set_seed(config['seed'])
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accelerator.print(config)
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accelerator.print(f"Using {accelerator.num_processes} GPUs")
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tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name'], model_max_length=config['max_length'])
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# if no pad token, set it to eos
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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with accelerator.main_process_first():
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train_dataloader, val_dataloader = load_data(config, tokenizer)
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checkpoint = config["gradient_checkpointing"]
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model = AutoModelForCausalLM.from_pretrained(config["model_name"],
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use_cache=False if checkpoint else True,
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trust_remote_code=True)
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if checkpoint:
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model.gradient_checkpointing_enable()
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if config["lora"]:
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peft_config = LoraConfig(
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# should R be configurable?
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task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
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)
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model = get_peft_model(model, peft_config)
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model.print_trainable_parameters()
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optimizer_cls = (
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AdamW
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if accelerator.state.deepspeed_plugin is None
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or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
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else DummyOptim
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)
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# karpathy doesn't decay embeddding, maybe we should exclude
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# https://github.com/karpathy/minGPT/commit/bbbdac74fa9b2e55574d70056163ffbae42310c1#diff-2075fa9c224b395be5bda85544dd36572b59c76c54562819eadadbf268602834R157s
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optimizer = optimizer_cls(model.parameters(), lr=config["lr"], weight_decay=config["weight_decay"])
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if accelerator.state.deepspeed_plugin is not None:
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gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[
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"gradient_accumulation_steps"
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]
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# decay to min_lr instead of 0
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lr_ratio = config["min_lr"] / config["lr"]
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accelerator.print(f"Len of train_dataloader: {len(train_dataloader)}")
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total_num_steps = (len(train_dataloader) / gradient_accumulation_steps) * config["num_epochs"]
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# instead of decaying to zero, decay to ratio of min_lr / lr
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total_num_steps += int(total_num_steps * lr_ratio) + config["warmup_steps"]
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accelerator.print(f"Total training steps: {total_num_steps}")
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# Creates Dummy Scheduler if `scheduler` was spcified in the config file else creates `args.lr_scheduler_type` Scheduler
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if (
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accelerator.state.deepspeed_plugin is None
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or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
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):
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scheduler = get_scheduler(
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name="cosine",
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optimizer=optimizer,
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num_warmup_steps=config["warmup_steps"] * accelerator.num_processes,
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num_training_steps=total_num_steps,
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)
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else:
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scheduler = DummyScheduler(
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optimizer, total_num_steps=config["warmup_steps"], warmup_num_steps=config["warmup_steps"]
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)
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model, optimizer, train_dataloader, val_dataloader, scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, val_dataloader, scheduler
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)
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# setup for saving training states in case preemption
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accelerator.register_for_checkpointing(scheduler)
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if config["checkpoint"]:
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accelerator.load_state(config["checkpoint"])
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accelerator.print(f"Resumed from checkpoint: {config['checkpoint']}")
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path = os.path.basename(config["train_args"]["resume_from_checkpoint"])
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training_difference = os.path.splitext(path)[0]
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resume_step = int(training_difference.replace("step_", ""))
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accelerator.skip_first_batches(train_dataloader, resume_step)
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accelerator.print(f"Resuming from step {resume_step}")
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# log gradients
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if accelerator.is_main_process and config["wandb"]:
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wandb.watch(model, log_freq=config["log_grads_every"], log="all")
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for epoch in range(config["num_epochs"]):
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train_loss = MeanMetric(nan_strategy="error").to(model.device)
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for step, batch in enumerate(tqdm(train_dataloader)):
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model.train()
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outputs = model(**batch)
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loss = outputs.loss
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# gather loss before backprop in case of gradient accumulation
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loss_values = accelerator.gather_for_metrics({"loss": loss.detach().float()})
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train_loss.update(loss_values["loss"])
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loss = loss / gradient_accumulation_steps
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accelerator.backward(loss)
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# get gradient norm of all params
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# log LR in case something weird happens
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if step > 0 and step % (config["eval_every"] // 10) == 0:
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if config["wandb"]:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.log({"lr": scheduler.get_last_lr()[0]}, step=curr_step)
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if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
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optimizer.step()
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scheduler.step()
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optimizer.zero_grad()
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if step > 0 and step % config["save_every"] == 0:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.save_state(f"{config['output_dir']}/step_{curr_step}")
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if step > 0 and (step % config["eval_every"] == 0 or step == len(train_dataloader) - 1):
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val_loss = evaluate(model, val_dataloader)
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log_train = {
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"train_loss": train_loss.compute()
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}
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log_val = {
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"val_loss": val_loss.compute()
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}
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if config["wandb"]:
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curr_step = step + epoch * len(train_dataloader)
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accelerator.log({**log_train, **log_val}, step=curr_step)
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accelerator.print(f"Current LR: {scheduler.get_last_lr()[0]}")
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accelerator.print(format_metrics(log_train, "train", f" step {step} "))
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accelerator.print(format_metrics(log_val, "val", f" step {step} "))
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train_loss.reset()
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accelerator.print(f"Epoch {epoch} finished")
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accelerator.print(f"Pushing to HF hub")
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accelerator.wait_for_everyone()
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unwrapped_model = accelerator.unwrap_model(model)
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try:
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if accelerator.is_main_process:
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unwrapped_model.push_to_hub(config["save_name"] + f"-epoch_{epoch}", private=True)
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except Exception as e:
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accelerator.print(e)
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accelerator.print(f"Failed to push to hub")
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unwrapped_model.save_pretrained(
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f"{config['output_dir']}/epoch_{epoch}",
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is_main_process=accelerator.is_main_process,
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save_function=accelerator.save,
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state_dict=accelerator.get_state_dict(model),
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)
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accelerator.wait_for_everyone()
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unwrapped_model = accelerator.unwrap_model(model)
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unwrapped_model.save_pretrained(
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f"{config['output_dir']}/final",
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is_main_process=accelerator.is_main_process,
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save_function=accelerator.save,
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state_dict=accelerator.get_state_dict(model),
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)
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accelerator.end_training()
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if __name__ == "__main__":
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# parse arguments by reading in a config
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parser = ArgumentParser()
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parser.add_argument("--config", type=str, default="config.yaml")
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args = parser.parse_args()
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config = read_config(args.config)
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if config["wandb"]:
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accelerator = Accelerator(log_with="wandb")
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accelerator.init_trackers(
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project_name=config["wandb_project_name"],
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config=config,
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init_kwargs={"wandb": {"entity": config["wandb_entity"]}},
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)
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else:
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accelerator = Accelerator()
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train(accelerator, config=config)
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